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Masoudi P.,University of Shahrood | Masoudi P.,Iranian Offshore Oil Company IOOC | Masoudi P.,University of Tehran | Asgarinezhad Y.,University of Shahrood | Tokhmechi B.,University of Shahrood
Arabian Journal of Geosciences | Year: 2014

The more accurate feature identification, the more precise reservoir characterisation. Porosity, permeability and other rock properties could be estimated and classified by analytical and intelligent methods. Feature selection plays a vital role in the process of identification. In this work, two goals are followed: first, developing Bayesian Network, K2 algorithm, as a complementary means (not an alternative) to find interrelationships of petrophysical parameters and second, feature conditioning for estimating porosity and permeability, vug and fracture detection, and net pay determination. Due to the results, bulk density log is introduced as the most important feature for characterizing the reservoir because it is found useful for identifying all the studied reservoir features. © 2014 Saudi Society for Geosciences. Source

Gholami R.,Curtin University Australia | Moradzadeh A.,University of Tehran | Rasouli V.,Curtin University Australia | Hanachi J.,Iranian Offshore Oil Company IOOC
Journal of Applied Geophysics | Year: 2014

Conventionally, high frequency Dipole Shear sonic Imager (DSI) logs are used for anisotropic modeling where fast and slow shear wave's velocities are required. However, the results obtained from a DSI log are restricted to a specific and possibly short interval of the wellbore. The aims of this paper are to use Vertical Seismic Profile (VSP) data and show its application in geomechanical analysis of subsurface layers under anisotropic condition. After processing and separating upgoing and downgoing P- and S-waves, a methodology based Vertical Transverse Isotropic (VTI) condition was presented to determine elastic stiffness parameters. Having stiffness parameters determined, elastic modulus, strength and in-situ stress parameters were estimated and calibrated against the field and core sample data. Although the VSP based geomechanical parameters were calibrated against the real field data, the accuracy of the method cannot be as much as that of the well logs. However, the method presented in this paper may become a very good asset for geomechanical evaluation of the intervals where well log data are not available. © 2014 Elsevier B.V. Source

Zohoorparvaz A.,Iranian Offshore Oil Company IOOC | Arastoo A.,Pars Oil And Gas Company | Sahraei E.,University of Tabriz
Petroleum Science and Technology | Year: 2013

Waterflooding in heterogeneous heavy oil reservoirs usually encounters the unfavorable mobility ratio resulting in early breakthrough and poor sweep efficiency. In such condition, injection of a viscous pre-flush is recommended. For this purpose, the use of emulsions as a mobility control agent has been studied here. Investigating the proper rheological behavior of invert water-in-oil emulsions, four different samples with 50%, 60%, 70%, and 80% water-cut were prepared and more stable products were injected into high permeable heavy oil saturated waterflooded sandpacks. Experiment results revealed that invert water-in-oil emulsions with high viscosities could help displacement process and increase oil recovery factor. © 2013 Copyright Taylor and Francis Group, LLC. Source

Mehrabi H.,University of Tehran | Rahimpour-Bonab H.,University of Tehran | Hajikazemi E.,Iranian Offshore Oil Company IOOC | Jamalian A.,University of Tehran
Facies | Year: 2015

In the Zagros area and the Persian Gulf, Upper Cretaceous carbonate sequences are among the most important hydrocarbon reservoirs. In this study, facies analysis and stratigraphic interpretation of these sequences, Cenomanian-Santonian in age, have been carried out in subsurface sections from various parts of the Zagros area (including the Dezful Embayment and Fars Province) and the Persian Gulf. To have a better understanding about the facies variations at the regional scale, depositional facies of these formations have been determined and grouped as facies associations. Frequency analyses of depositional facies and their characteristics reveal considerable variations in the study area. These are interpreted to have resulted from the combined effects of paleoenvironmental conditions and platform configuration. The overall depositional model of these formations is that of a carbonate ramp, which was likely homoclinal for the Sarvak and distally-steepened for the Ilam Formation. The isopach maps of the studied intervals are depicted based on the available data from hundreds of drilled wells, surface sections and seismic interpretations. Large scale variations in facies and thicknesses of the studied formations are interpreted to be controlled by regional tectonic evolution and sea-level fluctuations during the Upper Cretaceous. © 2015, Springer-Verlag Berlin Heidelberg. Source

Masoudi P.,Iranian Offshore Oil Company IOOC | Masoudi P.,University of Tehran | Arbab B.,Offshore Oil Company IOOC | Mohammadrezaei H.,Offshore Oil Company IOOC | Mohammadrezaei H.,Iranian Offshore Oil Company IOOC
Journal of Petroleum Science and Engineering | Year: 2014

Determining productive zones has always been a challenge for petrophysicists. On the other hand, Artificial Neural Networks are powerful tools in solving identification problems. In this paper, pay zone determination is defined as an identification problem, and is tried to solve it by trained Neural Networks. Proposed methodology is applied on two datasets: one belongs to carbonate reservoir of Mishrif, the other belongs to sandy Burgan reservoir. The results showed high precision in classifying productive zones in predefined classes with Classification Correctness Rate of more than 85% in both geological conditions. Applicability of proposed pay zone determination procedure in carbonate environment is a great advantage of developed methodology. Fuzzified output, being independent of core tests and verification with well tests results are of other advantages of the Neural Network-based method of pay zone detection. © 2014 Elsevier B.V. Source

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